text: This relies on the availability of experimentally determined 3D structures or high quality homology models. A number of methods exist for this assessment of druggability but all of them consist of three main components:
  #Identifying cavities or pockets on the structure
  #Calculating physicochemical and geometric properties of the pocket
  #Assessing how these properties fit a training set of known druggable targets, typically using machine learning algorithms
  Early work on introducing some of the parameters of structure-based druggability came from Abagyan and coworkers and then Fesik and coworkers, the latter by assessing the correlation of certain physicochemical parameters with hits from an NMR-based fragment screen. There has since been a number of publications reporting related methodologies.
  There are several commercial tools and databases for structure-based druggability assessment. A publicly available database of pre-calculated druggability assessments for all structural domains within the Protein Data Bank (PDB) is provided through the ChEMBL's DrugEBIlity portal.
  Structure-based druggability is usually used to identify suitable binding pocket for a small molecule; however, some studies have assessed 3D structures for the availability of grooves suitable for binding helical mimetics. This is an increasingly popular approach in addressing the druggability of protein-protein interactions.